Abstract:The rapid evolution of IT and security demands robust data infrastructure to handle increasing volumes of telemetry and logs, driven further by AI advancements. However, most organizations lack the infrastructure to manage this data surge effectively.
Traditional data collection methods are fragmented and inefficient, creating silos and complicating data integration. Manual parsing and routing of diverse data sources become unsustainable, hampering analytics and data utilization. Moreover, regulatory requirements for prolonged data storage add to the complexity and cost, leaving little budget for modernization. Combining an iterative modernization strategy with a data maturity model provides a clear roadmap.
This approach helps organizations understand their current state, identify urgent modernization areas, and measure progress. Leaders, architects, and operators can systematically enhance data management capabilities, aligning efforts with organizational goals.
What You’ll Learn
-What data modernization is and how it impacts the work IT and security professionals do every day
-What a data maturity model is, what different states of maturity look like, how to determine where an organization is at, and how to measure progress
-Tools and techniques to de-risk the upgrade process
-Strategies for aligning project, department, and enterprise goals
